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The context detector utilizes the concept of profiles, which are groups of similarly grouped data points generated by a multivariate clustering algorithm.
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The sensor profiles are defined using a multivariate clustering algorithm; the algorithm is multivariate to include the sensors multidimensional contextual metadata which may include location, building, ownership company, time of year, time of day, and weather phenomena.
Similarity relationships were investigated by means of multivariate clustering, similarity percentage analysis and nm-MDS ordination; the two experimental designs have been tested by permutational MANOVA and analysis of dispersion (PERMDISP).
This was facilitated by a multivariate statistical technique, cluster analysis, which was specified to divide the sample into no more than 2000 relatively homogenous subsets (i.e. clusters) of similar articles.
As a result, automated multivariate clustering has become highly desirable for objective and reproducible assessment of high dimensional FC data.
A final cohesive slice is considered as a normal multivariate cluster if all components have a normal distribution.
If scaled and centered data are not well-approximated by a multivariate normal distribution (multiple clusters within the data can also violate the normality assumption), rotated axes are more likely to pass through the centre of the multivariate data cloud when obtained using robust covariance estimators to identify the most variable traits using alternative metrics [ 35].
Our method overcomes these problems using a multivariate normal clustering method based on the Akaike Information Criterion (AIC) [ 31].
First, the multivariate distribution of the asset returns is fitted by a multivariate distribution.
Risk factors were evaluated by a multivariate logistic regression model.
All these techniques are strongly supported by a multivariate approach.
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